
HA:
Historical Average model, which models time series as a periodic process and uses weighted averages from previous periods as predictions for future periods.

VAR:
Vector Auto-Regression [1]}, [2]} assumes that the past time series is stationary and estimates the relationship between the time series and their lag value. [3]}

SVR: Support Vector Regression (SVR) uses linear support vector machine for classical time series regression task.

FC-LSTM [4]}: Long Short-Term Memory network with fully connected hidden units is a well-known network architecture that is powerful in capturing sequential dependency.

DCRNN [5]}: Diffusion Convolutional Recurrent Neural Network [5]} replaces the fully connected layer in GRU [7]} by diffusion convolutional layer to form a new Diffusion Convolutional Gated Recurrent Unit (DCGRU).

Graph WaveNet [8]}: Graph WaveNet stacks gated temporal convolutional layer and GCN layer by layer to jointly capture the spatial and temporal dependencies.

ASTGCN [9]}: ASTGCN combines the spatial-temporal attention mechanism to capture the dynamic spatial-temporal characteristics simultaneously.

STSGCN [10]}: STSGCN is proposed to effectively capture the localized spatial-temporal correlations and consider the heterogeneity in spatial-temporal data.

MTGNN [11]}: MTGNN extends Graph WaveNet through the mix-hop propagation layer in the spatial module, the dilated inception layer in the temporal module, and a more delicate graph learning layer.

GMAN [12]}: GMAN is an attention-based model which stacks spatial, temporal and transform attentions.

GTS [13]}: GTS learns a graph structure among multiple time series and forecasts them simultaneously with DCRNN.

